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IEEE International Conference on Mechatronics and Automation

作者： Zhang, Rui Gao, Hui Harbin Univ Sci & Technol Sch Automat Harbin 150080 Peoples R China Harbin Inst Technol Sch Commun Sci & Engn Harbin 150090 Peoples R China

ISBN: (纸本)9781424408276

For optimzing complex functions with high-dimension, a real-coded quantum evolutionary algorithm (RCQEA) is proposed on the basis of the concept and principles of quantum computing such as qubits and superposition of states. Firstly, in this algorithm, real-coded triploid chromosomes, whose alleles are composed of real variable and a pair of probability amplitudes of the correspinding states of one qubit, are constructed to keep the diversity of solution. Secondly, complementary double mutation operator (CDMO), which is designed according to a pair of probability amplitudes of the correspinding states of one qubit satisfying the normalization condition, as well as quantum rotation gate (QRG) are used to update chromosomes, which can treat the balance between exploration and exploitation. Thirdly, discrete crossover (DC) is employed to expand search space. Finally, "Hill-climbing" selection (HCS) is adopted to accelerate the convergence speed. Simulation results on 4 benchmark complex functions with high-dimension show that RCQEA is not only effective, efficient, but also very adaptive to the dimensions, and has the characteristics of rapider convergence, more powerful global search capability and better stability.

关键词： quantum computation quantum evolutionary algorithm real-coded quantum evolutionary algorithm function optimization

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An adaptive quantum evolutionary algorithm and its Application to Path Planning

An adaptive Quantum Evolutionary Algorithm and its Applicati...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Ming, Li Guangdong Univ Technol Sch Automat Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781479986965

The path planning problem is of essential significance for the theoretical research and practical applications of mobile robot navigation. However, it is found to be non-deterministic polynomial time hard (NP-hard) problem. Aiming at solving the problem of the large computational complexity, an adaptive quantum evolutionary algorithm with improved population initialization, adaptive quantum gate operation, crossover and mutation is presented to better the computing performance. The experimental simulation results have demonstrated that the proposed algorithm has high speed of convergence and good global search capability and thus proved that our algorithm is effective and feasible for the trajectory planning of mobile robot in obstacles environments.

关键词： path planning quantum evolutionary algorithm mobile robot quantum computation

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Self-organizing quantum evolutionary algorithm Based on quantum Dynamic Mechanism

Self-organizing Quantum Evolutionary Algorithm Based on Quan...

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International Conference on Artificial Intelligence and Computational Intelligence

作者： Liu, Sheng You, Xiaoming Shanghai Univ Engn Sci Sch Management Shanghai 200065 Peoples R China

ISBN: (纸本)9783642052521

A novel self-organizing quantum evolutionary algorithm based on quantum Dynamic mechanism for global optimization (DQEA) is proposed. Firstly, population is divided into subpopulations automatically. Secondly, by using co-evolution operator each subpopulation can obtain optimal solutions. Because of the quantum evolutionary algorithm with intrinsic adaptivity it can maintain quite nicely the population diversity than the classical evolutionary algorithm. In addition, it can help to accelerate the convergence speed because of the co-evolution by quantum dynamic mechanism. The searching technique for improving the performance of DQEA has been described;self-organizing algorithm has advantages in terms of the adaptability;reliability and the learning ability over traditional organizing algorithm. Simulation results demonstrate the superiority of DQEA in this paper.

关键词： quantum evolutionary algorithm quantum dynamic mechanism Self-organizing and co-evolution

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Improved quantum evolutionary algorithm Combined with Chaos and Its Application

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6th International Symposium on Neural Networks

作者： Xiao, Jianhua Nankai Univ Res Ctr Logist Tianjin 300071 Peoples R China

ISBN: (纸本)9783642015120

quantum evolutionary algorithm (QEA) has been developed rapidly and has been applied widely during the past decade. In this paper, an improved quantum evolutionary algorithm (IQEA) is presented based on particle swarm optimization (PSO) and chaos. The simulation results in solving DNA encoding demonstrate that the improved quantum evolutionary algorithm is valid and outperforms the quantum chaotic swarm evolutionary algorithm and conventional evolutionary algorithm. abstract environment.

关键词： quantum evolutionary algorithm Particle swarm optimization Chaos Optimization DNA encoding problem

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A L5-based Synchronous Cellular quantum evolutionary algorithm 7

A <i>L</i>5-based Synchronous Cellular Quantum Evolutionary ...

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7th International Conference on Information Technology in Medicine and Education (ITME)

作者： Qi, Feng Xu, Lili Shandong Normal Univ Jinan Peoples R China

ISBN: (纸本)9781467383028

In cellular quantum evolutionary algorithm(CQEA), the selection method of the neighbors for each individual affects its performance. Base on L5 neighbors and Asynchronous strategy, a L5-based synchronous cellular quantum evolutionary algorithm(LSCQEA) is proposed. In LSCQEA, each individual in a lattice and its four neighbors all undergo a quantum evolutionary algorithm, then population evolves by the overlapped neighbors of each individual. The performance of the proposed algorithm is tested on Knapsack Problem 14 benchmark functions, and simulation results show that LSCQEA has the similar performance but simpler complexity compared with other algorithms.

关键词： quantum evolutionary algorithm L5-based neighbors cellular structure

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quantum evolutionary algorithm Based Network Intrusion Detection

Quantum Evolutionary Algorithm Based Network Intrusion Detec...

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2010 3rd IEEE International Conference on Computer Science and Information Technology—ICCSIT 2010

作者： Xiangdong Yin Dept.of Computer science,Hunan University of science And Engineering Hunan,Shina

This quantum evolutionary algorithm is a newly developed method of probability optimization based on principles of quantum *** complex optimization problems,quantum evolutionary algorithm has a strong search capability and the most optimal *** intrusion detection problem can be converted into the one of data optimal classification,thus this paper introduces the quantum evolutionary algorithm to achieve the whole optimization process. The comparison simulation experiments have been carried out and the experimental results show the feasibility and effectiveness of methods proposed by this paper.

关键词： intrusion detection network evolutionary algorithm quantum evolutionary algorithm network intrusion detection

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quantum evolutionary algorithm for Chemical Parallel Flow Shop Scheduling Problem

Quantum Evolutionary Algorithm for Chemical Parallel Flow Sh...

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2015 AASRI International Conference on Industrial Electronics and Applications(IEA 2015)

作者： Qi Tang Peng Liu Jianxun Tang Xiang Li Management School Shenyang University of Technology College of Air Traffic Management Civil Aviation University of China

This paper studies the typical problem of integration of chemical batching and scheduling, which is characterized by multi-product, multi-stage, parallel production, production coordination between stages, limit of equipment production capacity and inventory capacity, parallel equipment selection and limitation of batch quantity. The optimization objective of this problem is to minimize the make-span by arranging batch number and size, allocating machines, making decision about the processing sequence and scheduling time of batch on a machine on the premise that the equipment production capacity, maximum inventory and material supply coordination are met. This paper presents modified quantum evolutionary algorithm(MQEA). The computational results show that the MQAE may find optimal or suboptimal solutions in a short run time for all the instances.

关键词： chemical industry quantum evolutionary algorithm batch scheduling

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Open Vehicle Routing Problem Using quantum evolutionary algorithm

Open Vehicle Routing Problem Using Quantum Evolutionary Algo...

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Global Conference on Digital Design and Manufacturing Technology

作者： Zhao, Y. W. Zhang, J. L. Peng, D. J. Zhejiang Univ Technol MOE Key Lab Mech Mfg & Automat Hangzhou 310014 Zhejiang Peoples R China

ISBN: (纸本)087849278X

Open vehicle routing problem is a kind of special vehicle routing problem, in which the vehicles do not return the depots after completing the task. Aiming at open vehicle routing problem, the mathematical model was founded by introducing virtual depots. A quantum evolutionary algorithm combined with local optimization algorithms was proposed in this paper, in which 0-1 matrix encoding was used to construct chromosomes, rotation gate with adaptively adjusting rotation angle was used to realize evolution, nearest neighbors and 2-Opt were incorporated to further improve solutions. Based on benchmark problems, the algorithm's parameters were discussed, and the computation result was compared to those of other algorithms. The Computation results indicated that the proposed algorithm was an efficient method for solving open vehicle routing problem.

关键词： Open vehicle routing problem quantum evolutionary algorithm Rotation gate Local optimization algorithm

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A Fast Watermarking algorithm Based on quantum evolutionary algorithm

A Fast Watermarking Algorithm Based on Quantum Evolutionary ...

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IEEE International Conference on Intelligent Computing and Intelligent Systems

作者： Wang, Zhiwen Li, Shaozi Cai, Qixian Su, Songzhi Liu, MeiZhen GXU Dept Comp & Engn Liuzhou Peoples R China Xiamen Univ Dept Comp Sci Xiamen Peoples R China Guangxi Univ Library Liuzhou Peoples R China Xiamen Univ Dept Cognit Sci Xiamen Peoples R China

ISBN: (纸本)9781424447541

There are limitations in smaller embedded information capacity, lower accurate rate for positioning embedded location and detection, slower running speed in traditional watermarking algorithm which combines spatial domain and transform domain based on characteristics of human visual system A Fast Watermarking algorithm based on quantum evolutionary algorithm is proposed and implemented We carry through simulation experiments by using the new algorithm brought forward in this article Results of experiments show that the new algorithm owns not only faster watermark image generation, but also have good sensitivity and robustness for all kinds of attacks The new algorithm is more flexible in application, can be embedded more information, can quicken computing speed, can easily operate and so on

关键词： quantum evolutionary algorithm wavelet transform quantification robustness Watermark Detection

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Bi-Level Multi-Objective Optimization Scheduling for Regional Integrated Energy Systems Based on quantum evolutionary algorithm

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ENERGIES 2021年第16期14卷 4740页

作者： Fan, Wen Liu, Qing Wang, Mingyu North China Elect Power Univ Baoding State Key Lab Alternate Elect Power Syst Renewabl Baoding 071003 Peoples R China

Integrated energy systems have become an important research topic in the pursuit of sustainable energy development. This paper examines regional integrated energy systems, presents the typical architecture of regional integrated energy systems, and builds an integrated energy system model. Two evaluation indexes are proposed: the integrated energy self-sufficiency rate and the expected energy deficiency index. Based on these evaluation indexes and taking into account the uncertainty of wind power generation, a bi-level optimization model based on meta-heuristic algorithms and multi-objective programming is established to solve the problem of regional integrated energy system planning under different load structures and for multi-period and multi-scenario operation modes. A quantum evolutionary algorithm is combined with genetic algorithms to solve the problem.

关键词： integrated energy system quantum evolutionary algorithm multi-objective programming bi-level model uncertainty

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